/******************************************************************************* * * MIT License * * Copyright (c) 2024 Advanced Micro Devices, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. * *******************************************************************************/ #pragma once #include #include #include #include template int32_t mloPReLUBackwardRunHost(const miopenTensorDescriptor_t inputDesc, const miopenTensorDescriptor_t weightDesc, const miopenTensorDescriptor_t doutputDesc, const miopenTensorDescriptor_t dinputDesc, const Tgpu* input, const Tgpu* weight, const Tgpu* doutput, Tcheck* dinput_host, Tcheck* dweight_host) { auto input_tv = miopen::get_inner_expanded_tv<5>(miopen::deref(inputDesc)); auto doutput_tv = miopen::get_inner_expanded_tv<5>(miopen::deref(doutputDesc)); auto dinput_tv = miopen::get_inner_expanded_tv<5>(miopen::deref(dinputDesc)); auto input_sz = miopen::deref(inputDesc).GetElementSize(); auto weight_sz = miopen::deref(weightDesc).GetElementSize(); auto weight_grad_collector = std::vector(input_sz); miopen::par_ford(input_sz)([&](int gid) { auto tensor_layout = tensor_layout_t<5>(input_tv, gid); float input_v = static_cast(input[input_tv.get_tensor_view_idx(tensor_layout)]); float grad_v = static_cast(doutput[doutput_tv.get_tensor_view_idx(tensor_layout)]); if(dinput_host) { float weight_v; if(weight_sz == 1) weight_v = static_cast(weight[0]); else weight_v = static_cast(weight[tensor_layout.layout[1]]); float input_grad_v = input_v > 0 ? grad_v : weight_v * grad_v; dinput_host[dinput_tv.get_tensor_view_idx(tensor_layout)] = static_cast(input_grad_v); } if(dweight_host) { weight_grad_collector[gid] = input_v > 0 ? 0 : input_v * grad_v; } }); if(dweight_host) { if(weight_sz == 1) { double sum = 0; for(int i = 0; i < input_sz; ++i) sum += static_cast(weight_grad_collector[i]); dweight_host[0] = static_cast(sum); } else { size_t inner_size = std::accumulate( &input_tv.size[2], &input_tv.size[4], 1ul, std::multiplies()); size_t outer_size = inner_size * input_tv.size[1]; miopen::par_ford(weight_sz)([&](int i) { double sum = 0; miopen::ford(input_tv.size[0])([&](int j) { miopen::ford(inner_size)([&](int k) { sum += static_cast( weight_grad_collector[j * outer_size + i * inner_size + k]); }); }); dweight_host[i] = static_cast(sum); }); } } return miopenStatusSuccess; }